Multi-GPU Workstations for Particleworks MPS
Purpose-built computational platforms for Moving Particle Simulation (MPS). Skip the tedious mesh generation and accelerate complex free-surface flow, oil sloshing, and powertrain lubrication modeling with massive VRAM architectures.
High-VRAM configurations for simulating oil splashing and churning losses in transmissions and EV motors.
Dense parallel compute for mapping conjugate heat transfer and thermal cooling jackets in electric vehicles.
GPU-accelerated modeling of water splashes, rain droplets, and aerodynamic resistance interactions.
High-throughput methods for analyzing the mixing of highly viscous flows, doughs, and non-Newtonian fluids.
Software Ecosystem & Turnkey Installation Support
Every Particleworks workstation ships with validated NVIDIA drivers, CUDA toolkit, and your specified OS. Our engineers verify solver stability and run benchmark simulations before handover.
We Validate Your MPS Environment
Skip the CUDA toolkit conflicts. Kirti Telnet provisions your hardware with validated NVIDIA proprietary drivers, OpenMPI, and specific co-simulation integrations, ensuring your Particleworks software runs at peak TFLOPS on day one.
Primary Solver
Particleworks
Best for: Mesh-less Fluid Dynamics & MPS
Acceleration Libraries
NVIDIA CUDA Toolkit, OpenMPI, OpenMP
Best for: Multi-GPU Data Parallelization
OpenMPI / OpenMP — Multi-GPU and multi-node parallelization frameworks
Co-Simulation Integration
RecurDyn, Ansys, Granuleworks
Best for: Multi-Body Dynamics & Granular Couplings
Ansys Mechanical / Fluent — Structural and fluid coupling for thermal-fluid interaction studies
Granuleworks — Discrete element method (DEM) coupling for granular material simulations
Supported Operating Systems
ISV Compliance
Best for: Production Deployment Stability
Red Hat Enterprise Linux (RHEL) — Enterprise-grade headless solver deployments
Ubuntu LTS — Developer-friendly Linux environment with CUDA support
Enterprise System Matrix — Particleworks MPS Platforms
Particleworks relies heavily on NVIDIA CUDA cores and GPU memory. The number of fluid particles you can simulate is strictly limited by available GPU VRAM. Our systems are aggressively configured with up to 4x professional GPUs to handle tens of millions of particles without out-of-memory errors.
Entry MPS Modeler
Quick-turnaround particle modeling for introductory MPS workflows, basic oil sloshing visualization, and small-scale free-surface flow studies.
RAM: 64 GB DDR5
GPU: 1x NVIDIA RTX 4500 Ada 24GB
Storage: 1 TB Gen4 NVMe
Starting Price
Advanced Powertrain Node
High-VRAM single-GPU workstation for detailed powertrain lubrication analysis, gearbox churning simulations, and e-motor cooling studies.
RAM: 128 GB DDR5 ECC (4-Channel)
GPU: 1x NVIDIA RTX 6000 Ada 48GB
Storage: 2 TB Gen5 NVMe
Starting Price
Heavy Fluid Solver (Dual-GPU)
Dual-GPU parallel compute for complex vehicle water wading, large-scale oil flow simulations, and multi-million particle free-surface dynamics.
RAM: 256 GB DDR5 ECC (8-Channel)
GPU: 2x NVIDIA RTX 5000 Ada 32GB
Storage: 2 TB Gen5 NVMe + 2TB Scratch
Starting Price
Extreme Particle Rig (Quad-GPU)
Quad-GPU powerhouse for extreme-scale MPS with tens of millions of particles, full-vehicle aerodynamic spray analysis, and industrial mixing simulations.
RAM: 512 GB DDR5 ECC (8-Channel)
GPU: 4x NVIDIA RTX 6000 Ada 48GB
Storage: 4 TB Enterprise NVMe RAID
Starting Price
Enterprise MPS Cluster Node
Rack-mounted multi-GPU server for batch-mode MPS solving, multi-user job scheduling, and inter-node parallel processing across enterprise simulation farms.
RAM: 1 TB DDR5 ECC Reg (12-Channel)
GPU: 4x NVIDIA L40S 48GB
Storage: 7.68 TB Enterprise U.2 NVMe
Starting Price
Engineering Disclaimer: All specifications are indicative and subject to component availability at the time of order. GPU VRAM capacity and multi-GPU scaling are application-dependent. Particle counts are approximate and vary with simulation complexity. Prices are tentative and exclude applicable taxes and shipping. Particleworks software licenses are procured separately.
Architectural Features & Performance
Linear Multi-GPU Acceleration
Moving Particle Simulation (MPS) is easily parallelizable. Particleworks scales almost perfectly across multiple GPUs, allowing you to cut computation times linearly by assigning the simulation to multi-GPU clusters.
The VRAM Particle Limit
Physical resolution in a mesh-less environment is dictated directly by GPU memory. High-VRAM GPUs like the RTX 6000 Ada (48GB) are critical to prevent memory bottlenecks when simulating complex interactions involving millions of particles.
High-Endurance Transient Storage
Solving for free-surface flows generates massive amounts of output data. Our workstations feature dedicated enterprise-grade NVMe scratch arrays with high TBW (Terabytes Written) endurance, preventing drive wear and stalling during extended simulation runs.
Frequently Asked Questions
Why does Particleworks require powerful GPUs?
Particleworks utilizes a mesh-less Moving Particle Simulation (MPS) method that is mathematically optimized for parallel calculation. The software natively leverages NVIDIA CUDA cores, allowing a single high-end GPU to perform the work of hundreds of CPU cores, making the GPU the most critical hardware component.
How much GPU VRAM do I need for my simulations?
The amount of VRAM directly dictates how many fluid particles you can model. For basic models, 24GB to 32GB is sufficient. However, for massive automotive or industrial scaling, we recommend 48GB GPUs (like the RTX 6000 Ada) or multi-GPU configurations to securely pool VRAM for tens of millions of particles.
What is the role of the CPU if the GPU does the solving?
The CPU sets up the simulation, manages the fluid phase data preparation, handles co-simulation links (like sending data to RecurDyn or Ansys Mechanical), and runs post-processing. A strong multi-core CPU ensures the GPUs are continuously fed with data without bottlenecking.
Can I use multiple GPUs for a single simulation?
Yes. Particleworks supports MPI and inter-node parallel processing, allowing it to harness multiple GPUs within a single workstation or across a cluster, drastically reducing computation time for large-scale models.
Request Custom Simulation Architecture
Talk to KTPL’s infrastructure engineers for a GPU-optimized workload assessment and hardware recommendation for your Particleworks MPS environment.
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